A model of the starburst amacrine cell for motion direction detection

The mechanism of motion direction detection for direction selective ganglion cells (DSGCs) is still not well-understood and under debate. Recent studies have elaborated the critical experimental evidence that the starburst amacrine cells (SACs) can trigger off the null-direction inhibition to DSGCs. In this study, a simple but effective neural model is introduced for the SACs to solve the motion direction detection problems, based on greyscale images in the visual scene. Virtual simulations demonstrate that the neural model is capable of detecting the motion direction of objects with different shapes, sizes, greyscales, and positions efficiently. To further demonstrate the feasibility and effectiveness of the model, the performance of the proposed model is compared with traditional artificial neural networks (ANNs). Experimental results show it can completely beat ANNs on motion direction detection problems, in terms of recognition accuracy, noise immunity, computational and learning costs, biological soundness, and reasonability.